WonderCoder of the month: Rebecca (Becks) Simpson

Each month we introduce you to a superwoman working in IT to share their journey and tips from the field. This month we have put Rebecca, or as friends call her – Becks, on the spotlight. Becks is working with Artifical Intelligence and has an interesting story of how it all came to be.

Hi! Tell us about what you do and who you are.

At the time of writing this, I was the Head of Screening AI for Brisbane-based startup Maxwell Plus,  leading a team focused on developing algorithms to help doctors make improved clinical decisions relating to cancer risk prediction and diagnosis. At the time of publishing, however, I will be on a work sabbatical – a 5-month break to recalibrate my skills and build some awesome robotics and AI projects before my next exciting gig. Some highlights will be a 7-week research internship at the Montreal Institute of Learning Algorithms investigating explainability of AI models and a 4-week mentoring position at the Hax Accelerator in China, helping get hardware startups up and running.


How long have you been in tech?

I first embarked on my journey into tech in 2010 when I started a degree in Mechatronics and have been involved in a number of different tech areas ever since.


How did you get into tech?

I originally had no plans or even an inkling that I would end up in a tech-related field!  Having studied arts, music and languages at high school and done quite well in them, I took up Linguistics majoring in Chinese and Japanese at the university. However, I became a bit frustrated by the lack of practical use that much of my research had and I became increasingly fascinated by the amazing applications of software, robotics and other STEM-related fields could have in helping humanity, so I decided to make the switch.


You studied Languages and Mechatronics from the beginning. Was it a big step to go from that to where you are today?

It was a big step, especially given that I hadn’t studied physics or advanced mathematics at high school and having studied a Bachelor of Arts for 4 years, I hadn’t touched any of the technical matter I’d need to succeed in an Engineering Degree. I would say what got me through was strong friendships and teamwork, dedication and passion for engineering and the end goal as well as curiosity and love of learning.


How has your journey in tech been?

My journey has been varied, interesting and rewarding so far!  I’ve done a number of technical roles from field engineer on land oil and gas rigs, machine learning and robotics intern at Australia’s premier scientific research organisation, CSIRO, to running a small software and electronics consultancy and being the first employee at a high growth AI for healthcare startup.


What are your daily activities?

At the moment, a lot of my daily activities revolve around technical management, so administering processes, conducting code reviews and performing company training.  However, my days involved anything from coding up new processes and automating existing ones to make the company run more efficiently to investigating new research into state-of-the-art machine learning algorithms that we could use to help our clinicians solve particular problems related to cancer risk calculation and diagnosis.  Plus I’ll be on a work sabbatical early next year and I’m looking to spend my days reading new ML papers, making software projects and deploying them, doing some research and learning as much as I can about the maths, statistics and theory of data science, machine learning and deep learning.


Could you share some of the challenges you’ve faced as a woman in tech?

Probably the biggest challenge is the imposter’s syndrome. Even though I realise that both men and women can suffer from this, it can sometimes be made worse for women by environments that already underestimate their abilities and assume they aren’t knowledgeable in their fields.  Of course, not every place I have worked in or had exposure to was like that at all, but even a couple of sour interactions can make it hard to push past your own insecurities.


How did you overcome them?

That’s a tough one! I’m not sure I’ve entirely gotten rid of my imposter syndrome, but definitely, exposure helps. The more you talk to people in your field, learn more, grow your skills, make mistakes and improve as a result, the more confident you’ll be in your own abilities – namely, getting a good handle on what you do know and what you don’t (i.e. those opportunities for growth!). And it’s also important to always remember if someone is disparaging about your abilities in a non-constructive way, that says more about them than it does about you. Everyone is allowed to suck as this coding thing on their way to greatness and no one has the right to hold you back with their negativity.


What’s your aspiration in your tech journey?

Make a big, positive impact!  I got into engineering to help the world and make life a little better for people so my next steps in this tech journey will be around rekindling that fire and just going for it. Build solutions in areas like food and water sustainability because that’s one thing I’m passionate about – making sure everyone has enough food and water without ruining the earth to get it, and technology is a great tool to help with that.


Please share with our readers why they should get a career in tech!

I see tech as a tool to achieve the dream career you want – one that encompasses your passions and interests but that can still provide financial security.  Plus there are always fun and interesting problems to be solved in most areas of tech, so if you want an exciting career journey where you can have a real impact, then a tech career is for you!


Any tips or advice for them?

Nearly every single person who starts coding or engineering or tech related pursuits will suck at it in the beginning. Never let that hold you back!  As someone who almost gave in to that feeling of “not being good enough to do this”, let me tell you that the only way to get better is to keep trying. For a handful of crazy geniuses it might come naturally, but for the majority, it takes hard work and dedication to learning. So keep curious and keep pushing!


Could you give us some ideas on how we can overcome the diversity gap in tech?

Follow the data – identify where exactly the gap is happening for starters. Is it because not enough diverse individuals come through from junior schooling or is it later, once they’re in the tech ecosystem that pressures from within the industry are driving them out?  Then dig deeper, ask questions, find out from the source why tech is being chosen as a career option or why people leave the industry entirely or what the bottlenecks are to keeping diversity up and closing the gaps.  Then work with the industry and with the individuals to solve those problems. My personal tips are

a) mentor the younger generations where you can, inspire them to pick tech as their first choice,
b) look outside your network for hiring candidates so you can get people from all walks of life to apply and not just your buddies from university and
c) look closely at whether anything in your workplace could be preventing a diverse workforce or making it hostile for diverse individuals and work to change that.


Do you currently practice computer programming and what is your preferred programming language?

Yes, I do! Almost daily if I can. I prefer to program in Python because it’s so versatile and I’m most comfortable with it. However, I do like a bit of React and the occasional C++ depending on the project. Getting back into the lower level programming for robotics is something I’m looking forward to during my work sabbatical too!


What are your visions for AI in the future and what are your greatest concerns?

I’d like to imagine a world where these powerful algorithms are used to solve some of humanity’s most pressing problems like pollution, food security, water resourcing, sustainable energy and accessible healthcare. I can see the AI for Social Good movement coming up big time too which is great to see.  As for concerns, mostly around algorithms used to make biased decisions or do things to the detriment of humanity or the earth, but I’m pleased to see many experts unanimously pushing in favour of FAT ML – Fairness, Transparency and Accountability in machine learning. The main thing will be ensuring the rest of the world, especially industries looking to adopt ML into their core business, keeps up with those important areas.


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